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Wilson RH, Rowland R, Kennedy GT, Campbell C, Joe VC, Chin TL, Burmeister DM, Christy RJ, Durkin AJ. Review of machine learning for optical imaging of burn wound severity assessment. JOURNAL OF BIOMEDICAL OPTICS 2024; 29:020901. [PMID: 38361506 PMCID: PMC10869118 DOI: 10.1117/1.jbo.29.2.020901] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Revised: 01/08/2024] [Accepted: 01/10/2024] [Indexed: 02/17/2024]
Abstract
Significance Over the past decade, machine learning (ML) algorithms have rapidly become much more widespread for numerous biomedical applications, including the diagnosis and categorization of disease and injury. Aim Here, we seek to characterize the recent growth of ML techniques that use imaging data to classify burn wound severity and report on the accuracies of different approaches. Approach To this end, we present a comprehensive literature review of preclinical and clinical studies using ML techniques to classify the severity of burn wounds. Results The majority of these reports used digital color photographs as input data to the classification algorithms, but recently there has been an increasing prevalence of the use of ML approaches using input data from more advanced optical imaging modalities (e.g., multispectral and hyperspectral imaging, optical coherence tomography), in addition to multimodal techniques. The classification accuracy of the different methods is reported; it typically ranges from ∼ 70 % to 90% relative to the current gold standard of clinical judgment. Conclusions The field would benefit from systematic analysis of the effects of different input data modalities, training/testing sets, and ML classifiers on the reported accuracy. Despite this current limitation, ML-based algorithms show significant promise for assisting in objectively classifying burn wound severity.
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Affiliation(s)
- Robert H. Wilson
- University of California, Irvine, Beckman Laser Institute and Medical Clinic, Irvine, California, United States
- University of California, Irvine, Department of Medicine, Orange, California, United States
- University of California, Irvine, Health Policy Research Institute, Irvine, California, United States
| | - Rebecca Rowland
- University of California, Irvine, Beckman Laser Institute and Medical Clinic, Irvine, California, United States
| | - Gordon T. Kennedy
- University of California, Irvine, Beckman Laser Institute and Medical Clinic, Irvine, California, United States
| | - Chris Campbell
- University of California, Irvine, Beckman Laser Institute and Medical Clinic, Irvine, California, United States
| | - Victor C. Joe
- UC Irvine Health Regional Burn Center, Orange, California, United States
| | | | - David M. Burmeister
- Uniformed Services University of the Health Sciences, School of Medicine, Bethesda, Maryland, United States
| | - Robert J. Christy
- UT Health San Antonio, Military Health Institute, San Antonio, Texas, United States
| | - Anthony J. Durkin
- University of California, Irvine, Beckman Laser Institute and Medical Clinic, Irvine, California, United States
- University of California, Irvine, Department of Biomedical Engineering, Irvine, California, United States
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Rozo A, Miskovic V, Rose T, Keersebilck E, Iorio C, Varon C. A Deep Learning Image-to-Image Translation Approach for a More Accessible Estimator of the Healing Time of Burns. IEEE Trans Biomed Eng 2023; 70:2886-2894. [PMID: 37067977 DOI: 10.1109/tbme.2023.3267600] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/18/2023]
Abstract
OBJECTIVE An accurate and timely diagnosis of burn severity is critical to ensure a positive outcome. Laser Doppler imaging (LDI) has become a very useful tool for this task. It measures the perfusion of the burn and estimates its potential healing time. LDIs generate a 6-color palette image, with each color representing a healing time. This technique has very high costs associated. In resource-limited areas, such as low- and middle-income countries or remote locations like space, where access to specialized burn care is inadequate, more affordable and portable tools are required. This study proposes a novel image-to-image translation approach to estimate burn healing times, using a digital image to approximate the LDI. METHODS This approach consists of a U-net architecture with a VGG-based encoder and applies the concept of ordinal classification. Paired digital and LDI images of burns were collected. The performance was evaluated with 10-fold cross-validation, mean absolute error (MAE), and color distribution differences between the ground truth and the estimated LDI. RESULTS Results showed a satisfactory performance in terms of low MAE ( 0.2370 ±0.0086). However, the unbalanced distribution of colors in the data affects this performance. SIGNIFICANCE This novel and unique approach serves as a basis for developing more accessible support tools in the burn care environment in resource-limited areas.
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Zimmermann N, Sieberth T, Dobay A. Automated wound segmentation and classification of seven common injuries in forensic medicine. Forensic Sci Med Pathol 2023:10.1007/s12024-023-00668-5. [PMID: 37378809 DOI: 10.1007/s12024-023-00668-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/14/2023] [Indexed: 06/29/2023]
Abstract
In forensic medical investigations, physical injuries are documented with photographs accompanied by written reports. Automatic segmentation and classification of wounds on these photographs could provide forensic pathologists with a tool to improve the assessment of injuries and accelerate the reporting process. In this pilot study, we trained and compared several preexisting deep learning architectures for image segmentation and wound classification on forensically relevant photographs in our database. The best scores were a mean pixel accuracy of 69.4% and a mean intersection over union (IoU) of 48.6% when evaluating the trained models on our test set. The models had difficulty distinguishing the background from wounded areas. As an example, image pixels showing subcutaneous hematomas or skin abrasions were assigned to the background class in 31% of cases. Stab wounds, on the other hand, were reliably classified with a pixel accuracy of 93%. These results can be partially attributed to undefined wound boundaries for some types of injuries, such as subcutaneous hematoma. However, despite the large class imbalance, we demonstrate that the best trained models could reliably distinguish among seven of the most common wounds encountered in forensic medical investigations.
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Affiliation(s)
- Norio Zimmermann
- Zurich Institute of Forensic Medicine, University of Zurich, Winterthurerstrasse 190/52, CH-8057, Zurich, Switzerland
| | - Till Sieberth
- Zurich Institute of Forensic Medicine, University of Zurich, Winterthurerstrasse 190/52, CH-8057, Zurich, Switzerland
- 3D Centre Zurich, University of Zurich, Winterthurerstrasse 190/52, CH-8057, Zurich, Switzerland
| | - Akos Dobay
- Zurich Institute of Forensic Medicine, University of Zurich, Winterthurerstrasse 190/52, CH-8057, Zurich, Switzerland.
- Forensic Machine Learning Technology Center, University of Zurich, Winterthurerstrasse 190/52, CH-8057, Zurich, Switzerland.
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Kairys A, Pauliukiene R, Raudonis V, Ceponis J. Towards Home-Based Diabetic Foot Ulcer Monitoring: A Systematic Review. SENSORS (BASEL, SWITZERLAND) 2023; 23:3618. [PMID: 37050678 PMCID: PMC10099334 DOI: 10.3390/s23073618] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Revised: 03/14/2023] [Accepted: 03/27/2023] [Indexed: 06/19/2023]
Abstract
It is considered that 1 in 10 adults worldwide have diabetes. Diabetic foot ulcers are some of the most common complications of diabetes, and they are associated with a high risk of lower-limb amputation and, as a result, reduced life expectancy. Timely detection and periodic ulcer monitoring can considerably decrease amputation rates. Recent research has demonstrated that computer vision can be used to identify foot ulcers and perform non-contact telemetry by using ulcer and tissue area segmentation. However, the applications are limited to controlled lighting conditions, and expert knowledge is required for dataset annotation. This paper reviews the latest publications on the use of artificial intelligence for ulcer area detection and segmentation. The PRISMA methodology was used to search for and select articles, and the selected articles were reviewed to collect quantitative and qualitative data. Qualitative data were used to describe the methodologies used in individual studies, while quantitative data were used for generalization in terms of dataset preparation and feature extraction. Publicly available datasets were accounted for, and methods for preprocessing, augmentation, and feature extraction were evaluated. It was concluded that public datasets can be used to form a bigger, more diverse datasets, and the prospects of wider image preprocessing and the adoption of augmentation require further research.
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Affiliation(s)
- Arturas Kairys
- Automation Department, Electrical and Electronics Faculty, Kaunas University of Technology, 51368 Kaunas, Lithuania
| | - Renata Pauliukiene
- Department of Endocrinology, Lithuanian University of Health Sciences, 50161 Kaunas, Lithuania
| | - Vidas Raudonis
- Automation Department, Electrical and Electronics Faculty, Kaunas University of Technology, 51368 Kaunas, Lithuania
| | - Jonas Ceponis
- Institute of Endocrinology, Lithuanian University of Health Sciences, 44307 Kaunas, Lithuania
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Li Z, Huang J, Tong X, Zhang C, Lu J, Zhang W, Song A, Ji S. GL-FusionNet: Fusing global and local features to classify deep and superficial partial thickness burn. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:10153-10173. [PMID: 37322927 DOI: 10.3934/mbe.2023445] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
Burns constitute one of the most common injuries in the world, and they can be very painful for the patient. Especially in the judgment of superficial partial thickness burns and deep partial thickness burns, many inexperienced clinicians are easily confused. Therefore, in order to make burn depth classification automated as well as accurate, we have introduced the deep learning method. This methodology uses a U-Net to segment burn wounds. On this basis, a new thickness burn classification model that fuses global and local features (GL-FusionNet) is proposed. For the thickness burn classification model, we use a ResNet50 to extract local features, use a ResNet101 to extract global features, and finally implement the add method to perform feature fusion and obtain the deep partial or superficial partial thickness burn classification results. Burns images are collected clinically, and they are segmented and labeled by professional physicians. Among the segmentation methods, the U-Net used achieved a Dice score of 85.352 and IoU score of 83.916, which are the best results among all of the comparative experiments. In the classification model, different existing classification networks are mainly used, as well as a fusion strategy and feature extraction method that are adjusted to conduct experiments; the proposed fusion network model also achieved the best results. Our method yielded the following: accuracy of 93.523, recall of 93.67, precision of 93.51, and F1-score of 93.513. In addition, the proposed method can quickly complete the auxiliary diagnosis of the wound in the clinic, which can greatly improve the efficiency of the initial diagnosis of burns and the nursing care of clinical medical staff.
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Affiliation(s)
- Zhiwei Li
- School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China
| | - Jie Huang
- Department of Burn Surgery, the First Affiliated Hospital of Naval Medical University, Shanghai 200444, China
| | - Xirui Tong
- Department of Burn Surgery, the First Affiliated Hospital of Naval Medical University, Shanghai 200444, China
| | - Chenbei Zhang
- School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China
| | - Jianyu Lu
- Department of Burn Surgery, the First Affiliated Hospital of Naval Medical University, Shanghai 200444, China
| | - Wei Zhang
- Department of Burn Surgery, the First Affiliated Hospital of Naval Medical University, Shanghai 200444, China
| | - Anping Song
- School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China
| | - Shizhao Ji
- Department of Burn Surgery, the First Affiliated Hospital of Naval Medical University, Shanghai 200444, China
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Liang J, Li R, Wang C, Zhang R, Yue K, Li W, Li Y. A Spiking Neural Network Based on Retinal Ganglion Cells for Automatic Burn Image Segmentation. ENTROPY (BASEL, SWITZERLAND) 2022; 24:1526. [PMID: 36359618 PMCID: PMC9689035 DOI: 10.3390/e24111526] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Revised: 10/17/2022] [Accepted: 10/18/2022] [Indexed: 06/16/2023]
Abstract
Burn is a common traumatic disease. After severe burn injury, the human body will increase catabolism, and burn wounds lead to a large amount of body fluid loss, with a high mortality rate. Therefore, in the early treatment for burn patients, it is essential to calculate the patient's water requirement based on the percentage of the burn wound area in the total body surface area (TBSA%). However, burn wounds are so complex that there is observer variability by the clinicians, making it challenging to locate the burn wounds accurately. Therefore, an objective, accurate location method of burn wounds is very necessary and meaningful. Convolutional neural networks (CNNs) provide feasible means for this requirement. However, although the CNNs continue to improve the accuracy in the semantic segmentation task, they are often limited by the computing resources of edge hardware. For this purpose, a lightweight burn wounds segmentation model is required. In our work, we constructed a burn image dataset and proposed a U-type spiking neural networks (SNNs) based on retinal ganglion cells (RGC) for segmenting burn and non-burn areas. Moreover, a module with cross-layer skip concatenation structure was introduced. Experimental results showed that the pixel accuracy of the proposed reached 92.89%, and our network parameter only needed 16.6 Mbytes. The results showed our model achieved remarkable accuracy while achieving edge hardware affinity.
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Jiao H, Tian T, Chen C, Ji G. Effect of vacuum sealing drainage plus skin grafting on deep burns and analysis of risk factors for postoperative infection. Am J Transl Res 2022; 14:7477-7486. [PMID: 36398255 PMCID: PMC9641438] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Accepted: 09/08/2022] [Indexed: 06/16/2023]
Abstract
OBJECTIVE To analyze the effect of vacuum sealing drainage (VSD) plus skin grafting on deep burns and the risk factors of postoperative infection. METHODS A retrospective analysis was conducted on 124 patients with deep burns who were admitted to the Taizhou People's Hospital from February 2019 to February 2021. The 55 patients who underwent traditional dressing change therapy plus second-stage skin grafting became the control group. The remaining 69 patients treated with first stage VSD plus second-stage skin grafting became the observation group. Wound healing time, hospital stay, postoperative infection, wound closure success rate, pain degree, and scar hyperplasia were recorded and compared between the two groups. Logistic regression was used to analyze the risk factors of postoperative infection in patients. RESULTS Compared with the control group, the observation group had a shorter time of wound healing and hospital stay, lower postoperative wound infection rate and lung infection rate, higher success rate of wound closure, lower pain degree scores on the 7th day after the operation, and less scar hyperplasia (all P<0.05). Logistic regression analysis revealed that burn degree, treatment plan, proportion of burn area, and wound healing time were the factors affecting postoperative infection in patients. CONCLUSION VSD plus skin grafting have significant effects on deep burns. This combined treatment reduced the occurrence of the wound infection, relieved pain, shortened the healing time and hospitalization time, and reduced the proliferation of scars in the later stage.
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Affiliation(s)
- Hongbo Jiao
- Burn and Plastic Surgery, Taizhou People's Hospital No. 366 Taihu Road, Hailing District, Taizhou 225300, Jiangsu, China
| | - Tian Tian
- Burn and Plastic Surgery, Taizhou People's Hospital No. 366 Taihu Road, Hailing District, Taizhou 225300, Jiangsu, China
| | - Chuanjun Chen
- Burn and Plastic Surgery, Taizhou People's Hospital No. 366 Taihu Road, Hailing District, Taizhou 225300, Jiangsu, China
| | - Geng Ji
- Burn and Plastic Surgery, Taizhou People's Hospital No. 366 Taihu Road, Hailing District, Taizhou 225300, Jiangsu, China
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